\n| Editorial calendar planning<\/td>\n | Opus<\/td>\n | Complex multi-step reasoning required<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n The pattern is simple:<\/strong><\/p>\n\n- Structured data task?<\/em> \u2192 Flash<\/li>\n
- Needs live web access?<\/em> \u2192 Kimi K2.5 or Sonar Pro<\/li>\n
- Writing or editing?<\/em> \u2192 Sonnet<\/li>\n
- Complex reasoning?<\/em> \u2192 Opus<\/li>\n<\/ul>\n
Most teams default to using one model for everything. That\u2019s like using a sledgehammer for every nail. Matching models to tasks cuts costs without cutting quality.<\/p>\n Real Cost Data From Our Operation<\/h2>\nHere\u2019s what our actual monthly AI spend looks like across all SEO operations:<\/p>\n \n \n\n\n| Model<\/th>\n | % of Tasks<\/th>\n | Monthly Tokens<\/th>\n | Monthly Cost<\/th>\n<\/tr>\n<\/thead>\n | \n\n| Flash<\/td>\n | 75%<\/td>\n | ~12M<\/td>\n | $1.20<\/td>\n<\/tr>\n | \n| Kimi K2.5<\/td>\n | 10%<\/td>\n | ~4M<\/td>\n | $2.40<\/td>\n<\/tr>\n | \n| Sonnet<\/td>\n | 12%<\/td>\n | ~4M<\/td>\n | $12.00<\/td>\n<\/tr>\n | \n| Opus<\/td>\n | 3%<\/td>\n | ~1M<\/td>\n | $6.00<\/td>\n<\/tr>\n | \n| Sonar Pro<\/td>\n | Varies<\/td>\n | ~2M<\/td>\n | $3.00<\/td>\n<\/tr>\n | \nTotal<\/strong><\/td>\n| <\/td>\n | ~23M<\/strong><\/td>\n$24.60<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n TOTAL MONTHLY AI SPEND<\/p>\n $24.60<\/p>\n For ~23M tokens across 5 models<\/p>\n<\/div>\n If we ran everything through Sonnet, that same 23M tokens would cost roughly $69. Through Opus? Over $300.<\/p>\n Model routing isn\u2019t a nice-to-have optimization. It\u2019s the difference between a $25 monthly AI bill and a $300 one \u2014 with no improvement in output quality for the vast majority of tasks.<\/p>\n FAQ<\/h2>\nWhich AI model is best for SEO content writing?<\/strong><\/p>\nClaude Sonnet 4.5 produces the highest-quality SEO content in our testing. It handles blog posts, meta descriptions, title tags, and FAQ sections better than any other model we\u2019ve evaluated. The writing is natural, follows briefs accurately, and consistently scores above our quality thresholds.<\/p>\n Is Gemini Flash good enough for SEO?<\/strong><\/p>\nYes \u2014 for structured SEO tasks. Flash handles keyword extraction, data processing, classification, and schema markup generation just as well as more expensive models. It falls short on content writing and complex reasoning, but those represent a small fraction of total SEO workload.<\/p>\n How much does Claude Opus cost for SEO?<\/strong><\/p>\nClaude Opus 4.6 costs $15 per million input tokens \u2014 making it the most expensive model on our list. We spend roughly $6\/month on Opus because we restrict it to less than 3% of tasks. It\u2019s reserved for architecture decisions and complex debugging, not routine SEO work.<\/p>\n Can I use only one model for all SEO tasks?<\/strong><\/p>\nYou can, but you\u2019ll either overpay or underperform. A single cheap model won\u2019t write quality content. A single expensive model will drain your budget on tasks that don\u2019t need it. The most cost-effective approach is routing different tasks to different models based on complexity.<\/p>\n What is Kimi K2.5?<\/strong><\/p>\nKimi K2.5 is a 1 trillion parameter Mixture-of-Experts model built by Moonshot AI. Its standout feature for SEO is native web browsing \u2014 it can fetch and analyze live web pages without requiring a separate scraping tool. At $0.60\/1M tokens, it\u2019s positioned between budget models like Flash and premium models like Sonnet.<\/p>\n | | | |